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    The Computer Vision Digest: Machine Learning

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      Machine Learning EURASIP JASP Special Issue on Machine Learning in Image Processing

    Anonymous writes "EURASIP JASP Special Issue on Machine Learning in Image Processing

    URL : http://www.hindawi.com/journals/asp/si/mlip.html

    Call for papers

    Images have always played an important role in human life since vision probably is human beings' most important sense. As a consequence, the field of image processing has numerous applications (medical, military, etc.). Nowadays and more than ever, images are everywhere and it is very easy for everyone to generate a huge amount of images thanks to the advances in digital technologies. With such a profusion of images, traditional image processing techniques have to cope with more complex problems and have to face with their adaptability according to human vision. Vision being complex, machine learning has emerged as a key component of intelligent computer vision programs when adaptation is needed (e.g., face recognition) . Among the existing methods, one can quote neural networks, hidden Markov models, kernel based methods, and so forth. However, this mainly concerns the computer vision field, the learning of which emulates high-level vision processes (e.g., visual information categorization or interpretation). But one can also incorporate learning in image processing to emulate low-level vision processes. We can quote edge detection, noise filtering, adaptive compression, and so on, as such potential issues. With the advent of image datasets and benchmarks, machine learning and image processing have recently received a lot of attention. An innovative integration of machine learning in image processing is very likely to have a great benefit to the field, which will contribute to a better understanding of complex images. The number of image processing algorithms that incorporate some learning components is expected to increase, as adaptation is needed. However, an increase in adaptation is often linked to an increase in complexity and one has to efficiently control any machine learning technique to properly adapt it to image processing problems. Indeed, processing huge amounts of images means being able to process huge quantities of data often of high dimensions, which is problematic for most machine learning techniques. Therefore, an interaction with the image data and with image priors is necessary to drive model selection strategies.

    The primary purpose of this special issue is to increase the awareness of image processing researchers to the impact of machine learning algorithms in low-level tasks. Papers submitted to this special issue have to carefully address the problem of model selection (features selection, parameter or hyperparameters estimation) for the machine learning technique under consideration.

    This special issue aims at providing original and high-quality submissions related, but not limited, to one or more of the following topics:

    * Machine learning in image filtering
    * Machine learning in image restoration
    * Machine learning in edge detection
    * Machine learning in image feature extraction
    * Machine learning in image segmentation
    * Machine learning in image compression
    * Machine learning driven by imaging applications.

    Moreover, since image databases created for benchmarking or for training are crucial for progress in both machine learning and image processing fields, the evaluation of the submitted papers will take that aspect into account (accessibility, quality, reproducibility) and the performance evaluation has to be carefully adressed.

    Authors should follow the EURASIP Journal on Advances in Signal Processing manuscript format described at the journal site http://www.hindawi.com/journals/asp/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://www.hindawi.com/mts/ according to the following timetable:

    Manuscript Due September 1, 2007
    First Round of Reviews December 1, 2007
    Publication Date March 1, 2008

    Guest Editors:

    Olivier Lezoray, Vision and Image Analysis (VAI) Team,Cherbourg Applied Sciences University Laboratory (LUSAC), 120 Rue de l'Exode, 50000 Saint-Lô, France

    Christophe Charrier, Vision and Image Analysis (VAI) Team, Cherbourg Applied Sciences University Laboratory (LUSAC), 120 Rue de l'Exode, 50000 Saint-Lô, France

    Hubert Cardot, Pattern Recognition and Image Analysis Team, Computer Science Laboratory (LI), Université François Rabelais de Tours, 64 avenue Jean Portalis, 37200 Tours, France

    Sébastien Lefèvre, Models Images Vision (MIV) Team, Image Sciences, Computer Sciences and Remote Sensing Laboratory (LSIIT), CNRS and Louis Pasteur University (Strasbourg), Pôle API, Bd. Brant, BP 10413, 67412 Illkirch, France"
      Posted by schestowitzon Tuesday, May 15 @ 03:35:51 BST (3600 reads)
    Read More... | 78 comments | Score: 0

      Machine Learning Identifying Similar Images - Search Utility Video

      Posted by schestowitzon Saturday, October 21 @ 17:28:13 BST (1077 reads)
    Read More... | 33 comments | Score: 1

      Machine Learning Studying Depth in Two-Dimensional Images

    An article titled "CMU Researchers Teach Computers To Perceive 3 Dimensions In 2D Image" has taken my attention.
    "Researchers in Carnegie Mellon University's School of Computer Science have discovered a way to help computers understand the geometric context of outdoor scenes, making them better comprehend what they see, reports SCIENCE DAILY"

      Posted by schestowitzon Saturday, June 17 @ 05:52:49 BST (1032 reads)
    Read More... | 88 comments | Score: 0

      Machine Learning Machine Learning Department

    As it turns out, machine learning has become a broad enough field to have its own specialised department at Carnegie Mellon. It will become the first machine learning department in the States.
    March 30, 2006, PITTSBURGH—Carnegie Mellon University’s School of Computer Science (SCS) is extending its leadership in the burgeoning field of machine learning by creating the nation’s first Machine Learning Department.
    Full story
      Posted by schestowitzon Friday, March 31 @ 06:15:52 BST (1176 reads)
    Read More... | 43 comments | Score: 0

      Machine Learning Automatically-Generated Algorithm Surpasses Human Intelligence

    Research at the University of Texas suggests that fingerprint improvement using genetic algorithms outperformed algorithms which had been hand-coded. The credibility of this study can be questioned, but it is nonetheless an interesting contension.

    Read full story - Computer-Generated Method Outperforms Human-Designed Program For Fingerprint Improvement
      Posted by schestowitzon Monday, September 12 @ 03:13:05 BST (1173 reads)
    Read More... | 101 comments | Score: 0


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